{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# stacking_fault_map_2D calculation style\n",
    "\n",
    "**Lucas M. Hale**, [lucas.hale@nist.gov](mailto:lucas.hale@nist.gov?Subject=ipr-demo), *Materials Science and Engineering Division, NIST*.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "The __stacking_fault_map_2D__ calculation style evaluates the full 2D generalized stacking fault map for an array of shifts along a specified crystallographic plane.  A regular grid of points is established and the generalized stacking fault energy is evaluated at each.\n",
    "\n",
    "### Version notes\n",
    "\n",
    "- 2018-07-09: Notebook added.\n",
    "- 2019-07-30: Description updated and small changes due to iprPy version.\n",
    "- 2020-05-22: Version 0.10 update - potentials now loaded from database.\n",
    "- 2020-09-22: Calculation updated to use atomman.defect.StackingFault class. Setup and parameter definition streamlined.\n",
    "\n",
    "### Additional dependencies\n",
    "\n",
    "### Disclaimers\n",
    "\n",
    "- [NIST disclaimers](http://www.nist.gov/public_affairs/disclaimer.cfm)\n",
    "- The system's dimension perpendicular to the fault plane should be large to minimize the interaction of the free surface and the stacking fault.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method and Theory\n",
    "\n",
    "First, an initial system is generated.  This is accomplished using atomman.defect.StackingFault, which\n",
    "\n",
    "1. Starts with a unit cell system.\n",
    "\n",
    "2. Generates a transformed system by rotating the unit cell such that the new\n",
    "   system's box vectors correspond to crystallographic directions, and filled\n",
    "   in with atoms to remain a perfect bulk cell when the three boundaries are\n",
    "   periodic.\n",
    "\n",
    "3. All atoms are shifted by a fractional amount of the box vectors if needed.\n",
    "\n",
    "4. A supercell system is constructed by combining multiple replicas of the\n",
    "   transformed system.\n",
    "\n",
    "5. The system is then cut by making one of the box boundaries non-periodic.  A limitation placed on the calculation is that the normal to the cut plane must correspond to one of the three Cartesian ($x$, $y$, or $z$) axes.  If true, then of the system's three box vectors ($\\vec{a}$, $\\vec{b}$, and $\\vec{c}$), two will be parallel to the plane, and the third will not.  The non-parallel box vector is called the cutboxvector, and for LAMMPS compatible systems, the following conditions can be used to check the system's compatibility:\n",
    "\n",
    "   - cutboxvector = 'c': all systems allowed.\n",
    "\n",
    "   - cutboxvector = 'b': the system's yz tilt must be zero.\n",
    "\n",
    "   - cutboxvector = 'a': the system's xy and xz tilts must be zero.\n",
    "\n",
    "A LAMMPS simulation performs an energy/force minimization on the system where the atoms are confined to only relax along the Cartesian direction normal to the cut plane.\n",
    "\n",
    "A mathematical fault plane parallel to the cut plane is defined in the middle of the system.  A generalized stacking fault system can then be created by shifting all atoms on one side of the fault plane by a vector, $\\vec{s}$.  The shifted system is then relaxed using the same confined energy/force minimization used on the non-shifted system.  The generalized stacking fault energy, $\\gamma$, can then be computed by comparing the total energy of the system, $E_{total}$, before and after $\\vec{s}$ is applied\n",
    "\n",
    "$$ \\gamma(\\vec{s}) = \\frac{E_{total}(\\vec{s}) - E_{total}(\\vec{0})}{A},$$\n",
    "\n",
    "where $A$ is the area of the fault plane, which can be computed using the two box vectors, $\\vec{a_1}$ and $\\vec{a_2}$, that are not the cutboxvector.\n",
    "\n",
    "$$A = \\left| \\vec{a_1} \\times \\vec{a_2} \\right|,$$\n",
    "\n",
    "Additionally, the relaxation normal to the glide plane is characterized using the center of mass of the atoms above and below the cut plane.  Notably, the component of the center of mass normal to the glide/cut plane is calculated for the two halves of the the system, and the difference is computed\n",
    "\n",
    "$$ \\delta = \\left<x\\right>^{+} - \\left<x\\right>^{-}.$$\n",
    "\n",
    "The relaxation normal is then taken as the change in the center of mass difference after the shift is applied.\n",
    "\n",
    "$$ \\Delta\\delta = \\delta(\\vec{s}) - \\delta(\\vec{0}).$$\n",
    "\n",
    "The stacking_fault_map_2D calculation evaluates both $\\gamma$ and $\\Delta\\delta$ for a complete 2D grid of $\\vec{s}$ values.  The grid is built by taking fractional steps along two vectors parallel to the shift plane.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Library imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries needed by the calculation. The external libraries used are:\n",
    "\n",
    "- [numpy](http://www.numpy.org/)\n",
    "\n",
    "- [matplotlib](https://matplotlib.org/)\n",
    "\n",
    "- [atomman](https://github.com/usnistgov/atomman)\n",
    "\n",
    "- [iprPy](https://github.com/usnistgov/iprPy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook last executed on 2021-03-08 using iprPy version 0.10.4\n"
     ]
    }
   ],
   "source": [
    "# Standard library imports\n",
    "from pathlib import Path\n",
    "import os\n",
    "import datetime\n",
    "\n",
    "# http://www.numpy.org/\n",
    "import numpy as np \n",
    "\n",
    "# https://matplotlib.org/\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# https://github.com/usnistgov/atomman \n",
    "import atomman as am\n",
    "import atomman.lammps as lmp\n",
    "import atomman.unitconvert as uc\n",
    "\n",
    "# https://github.com/usnistgov/iprPy\n",
    "import iprPy\n",
    "\n",
    "print('Notebook last executed on', datetime.date.today(), 'using iprPy version', iprPy.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2. Default calculation setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify calculation style\n",
    "calc_style = 'stacking_fault_map_2D'\n",
    "\n",
    "# If workingdir is already set, then do nothing (already in correct folder)\n",
    "try:\n",
    "    workingdir = workingdir\n",
    "\n",
    "# Change to workingdir if not already there\n",
    "except:\n",
    "    workingdir = Path('calculationfiles', calc_style)\n",
    "    if not workingdir.is_dir():\n",
    "        workingdir.mkdir(parents=True)\n",
    "    os.chdir(workingdir)\n",
    "            \n",
    "# Initialize connection to library\n",
    "library = iprPy.Library(load=['lammps_potentials'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Assign values for the calculation's run parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Specify system-specific paths\n",
    "\n",
    "- __lammps_command__ is the LAMMPS command to use.\n",
    "\n",
    "- __mpi_command__ MPI command for running LAMMPS in parallel. A value of None will run simulations serially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lammps_command = 'lmp_serial'\n",
    "mpi_command = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2. Load interatomic potential\n",
    "\n",
    "- __potential_name__ gives the name of the potential_LAMMPS reference record in the iprPy library to use for the calculation.  \n",
    "\n",
    "- __potential__ is an atomman.lammps.Potential object (required)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "potential_name = '1999--Mishin-Y--Ni--LAMMPS--ipr1'\n",
    "\n",
    "# Retrieve potential and parameter file(s)\n",
    "potential = library.get_lammps_potential(id=potential_name, getfiles=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3. Load initial unit cell system\n",
    "\n",
    "- __ucell__ is an atomman.System representing a fundamental unit cell of the system (required).  Here, this is loaded from the database for the prototype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avect =  [ 3.520,  0.000,  0.000]\n",
      "bvect =  [ 0.000,  3.520,  0.000]\n",
      "cvect =  [ 0.000,  0.000,  3.520]\n",
      "origin = [ 0.000,  0.000,  0.000]\n",
      "natoms = 4\n",
      "natypes = 1\n",
      "symbols = ('Ni',)\n",
      "pbc = [ True  True  True]\n",
      "per-atom properties = ['atype', 'pos']\n",
      "     id |   atype |  pos[0] |  pos[1] |  pos[2]\n",
      "      0 |       1 |   0.000 |   0.000 |   0.000\n",
      "      1 |       1 |   0.000 |   1.760 |   1.760\n",
      "      2 |       1 |   1.760 |   0.000 |   1.760\n",
      "      3 |       1 |   1.760 |   1.760 |   0.000\n"
     ]
    }
   ],
   "source": [
    "# Create ucell by loading prototype record\n",
    "ucell = am.load('crystal', potential=potential, family='A1--Cu--fcc', database=library)\n",
    "\n",
    "print(ucell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4. Specify the defect parameters\n",
    "\n",
    "- __hkl__ gives the Miller (hkl) or Miller-Bravais (hkil) plane to create the free surface on.\n",
    "\n",
    "- __cutboxvector__ specifies which of the three box vectors ('a', 'b', or 'c') is to be made non-periodic to create the free surface. \n",
    "\n",
    "- __shiftindex__ can be used for complex crystals to specify different termination planes.\n",
    "\n",
    "- __a1vect_uvw, a2vect_uvw__ specify two non-parallel Miller crystal vectors within the fault plane corresponding to full planar shifts from one perfect crystal configuration to another.  \n",
    "\n",
    "- __num_a1, num_a2__ specify how many measurements to make along a1vect_uvw, a2vect_uvw respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hkl = [1, 1, 1]\n",
    "cutboxvector = 'c'\n",
    "shiftindex = 0\n",
    "a1vect_uvw = [ 0.0,-0.5, 0.5]\n",
    "a2vect_uvw = [ 0.5,-0.5, 0.0]\n",
    "num_a1 = 20\n",
    "num_a2 = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5. Modify system\n",
    "\n",
    "- __sizemults__  list of three integers specifying how many times the ucell vectors of $a$, $b$ and $c$ are replicated in creating system.\n",
    "- __minwidth__ specifies a minimum width that the system should be along the cutboxvector direction. The given sizemult in that direction will be increased if needed to ensure that the system is at least this wide.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sizemults = [5, 5, 10]\n",
    "minwidth = uc.set_in_units(0.0, 'angstrom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6. Specify calculation-specific run parameters\n",
    "\n",
    "- __energytolerance__ is the energy tolerance to use during the minimizations. This is unitless.\n",
    "\n",
    "- __forcetolerance__ is the force tolerance to use during the minimizations. This is in energy/length units.\n",
    "\n",
    "- __maxiterations__ is the maximum number of minimization iterations to use.\n",
    "\n",
    "- __maxevaluations__ is the maximum number of minimization evaluations to use.\n",
    "\n",
    "- __maxatommotion__ is the largest distance that an atom is allowed to move during a minimization iteration. This is in length units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "energytolerance = 1e-8\n",
    "forcetolerance = uc.set_in_units(0.0, 'eV/angstrom')\n",
    "maxiterations = 10000\n",
    "maxevaluations = 100000\n",
    "maxatommotion = uc.set_in_units(0.01, 'angstrom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Define calculation function(s) and generate template LAMMPS script(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1. sfmin.template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('sfmin.template', 'w') as f:\n",
    "    f.write(\"\"\"#LAMMPS input script that performs an energy minimization\n",
    "#for a system with a stacking fault\n",
    "\n",
    "box tilt large\n",
    "\n",
    "<atomman_system_pair_info>\n",
    "\n",
    "<fix_cut_setforce>\n",
    "\n",
    "thermo_style custom step lx ly lz pxx pyy pzz pe\n",
    "thermo_modify format float %.13e\n",
    "\n",
    "compute peatom all pe/atom \n",
    "\n",
    "min_modify dmax <dmax>\n",
    "\n",
    "dump dumpit all custom <maxeval> <sim_directory>*.dump id type x y z c_peatom\n",
    "dump_modify dumpit format <dump_modify_format>\n",
    "\n",
    "minimize <etol> <ftol> <maxiter> <maxeval>\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2. stackingfaultrelax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stackingfaultrelax(lammps_command, system, potential,\n",
    "                       mpi_command=None, sim_directory=None,\n",
    "                       cutboxvector='c',\n",
    "                       etol=0.0, ftol=0.0,\n",
    "                       maxiter=10000, maxeval=100000,\n",
    "                       dmax=uc.set_in_units(0.01, 'angstrom'),\n",
    "                       lammps_date=None):\n",
    "    \"\"\"\n",
    "    Perform a stacking fault relaxation simulation for a single faultshift.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    lammps_command :str\n",
    "        Command for running LAMMPS.\n",
    "    system : atomman.System\n",
    "        The system containing a stacking fault.\n",
    "    potential : atomman.lammps.Potential\n",
    "        The LAMMPS implemented potential to use.\n",
    "    mpi_command : str, optional\n",
    "        The MPI command for running LAMMPS in parallel.  If not given, LAMMPS\n",
    "        will run serially.\n",
    "    sim_directory : str, optional\n",
    "        The path to the directory to perform the simulation in.  If not\n",
    "        given, will use the current working directory.\n",
    "    cutboxvector : str, optional\n",
    "        Indicates which of the three system box vectors, 'a', 'b', or 'c', has\n",
    "        the non-periodic boundary (default is 'c').  Fault plane normal is\n",
    "        defined by the cross of the other two box vectors.\n",
    "    etol : float, optional\n",
    "        The energy tolerance for the structure minimization. This value is\n",
    "        unitless. (Default is 0.0).\n",
    "    ftol : float, optional\n",
    "        The force tolerance for the structure minimization. This value is in\n",
    "        units of force. (Default is 0.0).\n",
    "    maxiter : int, optional\n",
    "        The maximum number of minimization iterations to use (default is \n",
    "        10000).\n",
    "    maxeval : int, optional\n",
    "        The maximum number of minimization evaluations to use (default is \n",
    "        100000).\n",
    "    dmax : float, optional\n",
    "        The maximum distance in length units that any atom is allowed to relax\n",
    "        in any direction during a single minimization iteration (default is\n",
    "        0.01 Angstroms).\n",
    "    lammps_date : datetime.date or None, optional\n",
    "        The date version of the LAMMPS executable.  If None, will be identified\n",
    "        from the lammps_command (default is None).\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    dict\n",
    "        Dictionary of results consisting of keys:\n",
    "        \n",
    "        - **'logfile'** (*str*) - The filename of the LAMMPS log file.\n",
    "        - **'dumpfile'** (*str*) - The filename of the LAMMPS dump file\n",
    "          of the relaxed system.\n",
    "        - **'system'** (*atomman.System*) - The relaxed system.\n",
    "        - **'E_total'** (*float*) - The total potential energy of the relaxed\n",
    "          system.\n",
    "    \n",
    "    Raises\n",
    "    ------\n",
    "    ValueError\n",
    "        For invalid cutboxvectors.\n",
    "    \"\"\"\n",
    "    # Build filedict if function was called from iprPy\n",
    "    try:\n",
    "        assert __name__ == pkg_name\n",
    "        calc = iprPy.load_calculation(calculation_style)\n",
    "        filedict = calc.filedict\n",
    "    except:\n",
    "        filedict = {}\n",
    "    \n",
    "    # Give correct LAMMPS fix setforce command\n",
    "    if cutboxvector == 'a':\n",
    "        fix_cut_setforce = 'fix cut all setforce NULL 0 0'    \n",
    "    elif cutboxvector == 'b':\n",
    "        fix_cut_setforce = 'fix cut all setforce 0 NULL 0'\n",
    "    elif cutboxvector == 'c':\n",
    "        fix_cut_setforce = 'fix cut all setforce 0 0 NULL'    \n",
    "    else: \n",
    "        raise ValueError('Invalid cutboxvector')\n",
    "    \n",
    "    if sim_directory is not None:\n",
    "        # Create sim_directory if it doesn't exist\n",
    "        sim_directory = Path(sim_directory)\n",
    "        if not sim_directory.is_dir():\n",
    "            sim_directory.mkdir()\n",
    "        sim_directory = sim_directory.as_posix()+'/'\n",
    "    else:\n",
    "        # Set sim_directory if is None\n",
    "        sim_directory = ''\n",
    "    \n",
    "    # Get lammps units\n",
    "    lammps_units = lmp.style.unit(potential.units)\n",
    "    \n",
    "    #Get lammps version date\n",
    "    if lammps_date is None:\n",
    "        lammps_date = lmp.checkversion(lammps_command)['date']\n",
    "    \n",
    "    # Define lammps variables\n",
    "    lammps_variables = {}\n",
    "    system_info = system.dump('atom_data',\n",
    "                              f=Path(sim_directory, 'system.dat').as_posix(),\n",
    "                              potential=potential,\n",
    "                              return_pair_info=True)\n",
    "    lammps_variables['atomman_system_pair_info'] = system_info\n",
    "    lammps_variables['fix_cut_setforce'] = fix_cut_setforce\n",
    "    lammps_variables['sim_directory'] = sim_directory\n",
    "    lammps_variables['etol'] = etol\n",
    "    lammps_variables['ftol'] = uc.get_in_units(ftol, lammps_units['force'])\n",
    "    lammps_variables['maxiter'] = maxiter\n",
    "    lammps_variables['maxeval'] = maxeval\n",
    "    lammps_variables['dmax'] = uc.get_in_units(dmax, lammps_units['length'])\n",
    "    \n",
    "    # Set dump_modify format based on dump_modify_version\n",
    "    if lammps_date < datetime.date(2016, 8, 3):\n",
    "        lammps_variables['dump_modify_format'] = '\"%i %i %.13e %.13e %.13e %.13e\"'\n",
    "    else:\n",
    "        lammps_variables['dump_modify_format'] = 'float %.13e'\n",
    "    \n",
    "    # Write lammps input script\n",
    "    template_file = 'sfmin.template'\n",
    "    lammps_script = Path(sim_directory, 'sfmin.in')\n",
    "    template = iprPy.tools.read_calc_file(template_file, filedict)\n",
    "    with open(lammps_script, 'w') as f:\n",
    "        f.write(iprPy.tools.filltemplate(template, lammps_variables,\n",
    "                                         '<', '>'))\n",
    "    \n",
    "    # Run LAMMPS\n",
    "    output = lmp.run(lammps_command, lammps_script.as_posix(), mpi_command,\n",
    "                     logfile=Path(sim_directory, 'log.lammps').as_posix())\n",
    "    \n",
    "    # Extract output values\n",
    "    thermo = output.simulations[-1]['thermo']\n",
    "    logfile = Path(sim_directory, 'log.lammps').as_posix()\n",
    "    dumpfile = Path(sim_directory, '%i.dump' % thermo.Step.values[-1]).as_posix()\n",
    "    E_total = uc.set_in_units(thermo.PotEng.values[-1],\n",
    "                              lammps_units['energy'])\n",
    "    \n",
    "    # Load relaxed system\n",
    "    system = am.load('atom_dump', dumpfile, symbols=system.symbols)\n",
    "    \n",
    "    # Return results\n",
    "    results_dict = {}\n",
    "    results_dict['logfile'] = logfile\n",
    "    results_dict['dumpfile'] = dumpfile\n",
    "    results_dict['system'] = system\n",
    "    results_dict['E_total'] = E_total\n",
    "    \n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4. stackingfaultmap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stackingfaultmap(lammps_command, ucell, potential, hkl,\n",
    "                     mpi_command=None, sizemults=None, minwidth=None, even=False,\n",
    "                     a1vect_uvw=None, a2vect_uvw=None, conventional_setting='p',\n",
    "                     cutboxvector='c', faultpos_rel=None, faultpos_cart=None,\n",
    "                     num_a1=10, num_a2=10, atomshift=None, shiftindex=None,\n",
    "                     etol=0.0, ftol=0.0, maxiter=10000, maxeval=100000,\n",
    "                     dmax=uc.set_in_units(0.01, 'angstrom')): \n",
    "    \"\"\"\n",
    "    Computes a generalized stacking fault map for shifts along a regular 2D\n",
    "    grid.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    lammps_command :str\n",
    "        Command for running LAMMPS.\n",
    "    ucell : atomman.System\n",
    "        The crystal unit cell to use as the basis of the stacking fault\n",
    "        configurations.\n",
    "    potential : atomman.lammps.Potential\n",
    "        The LAMMPS implemented potential to use.\n",
    "    hkl : array-like object\n",
    "        The Miller(-Bravais) crystal fault plane relative to ucell.\n",
    "    mpi_command : str, optional\n",
    "        The MPI command for running LAMMPS in parallel.  If not given, LAMMPS\n",
    "        will run serially.\n",
    "    sizemults : list or tuple, optional\n",
    "        The three System.supersize multipliers [a_mult, b_mult, c_mult] to use on the\n",
    "        rotated cell to build the final system. Note that the cutboxvector sizemult\n",
    "        must be an integer and not a tuple.  Default value is [1, 1, 1].\n",
    "    minwidth : float, optional\n",
    "        If given, the sizemult along the cutboxvector will be selected such that the\n",
    "        width of the resulting final system in that direction will be at least this\n",
    "        value. If both sizemults and minwidth are given, then the larger of the two\n",
    "        in the cutboxvector direction will be used. \n",
    "    even : bool, optional\n",
    "        A True value means that the sizemult for cutboxvector will be made an even\n",
    "        number by adding 1 if it is odd.  Default value is False.\n",
    "    a1vect_uvw : array-like object, optional\n",
    "        The crystal vector to use for one of the two shifting vectors.  If\n",
    "        not given, will be set to the shortest in-plane lattice vector.\n",
    "    a2vect_uvw : array-like object, optional\n",
    "        The crystal vector to use for one of the two shifting vectors.  If\n",
    "        not given, will be set to the shortest in-plane lattice vector not\n",
    "        parallel to a1vect_uvw.\n",
    "    conventional_setting : str, optional\n",
    "        Allows for rotations of a primitive unit cell to be determined from\n",
    "        (hkl) indices specified relative to a conventional unit cell.  Allowed\n",
    "        settings: 'p' for primitive (no conversion), 'f' for face-centered,\n",
    "        'i' for body-centered, and 'a', 'b', or 'c' for side-centered.  Default\n",
    "        behavior is to perform no conversion, i.e. take (hkl) relative to the\n",
    "        given ucell.\n",
    "    cutboxvector : str, optional\n",
    "        Indicates which of the three system box vectors, 'a', 'b', or 'c', to\n",
    "        cut with a non-periodic boundary (default is 'c').\n",
    "    faultpos_rel : float, optional\n",
    "        The position to place the slip plane within the system given as a\n",
    "        relative coordinate along the out-of-plane direction.  faultpos_rel\n",
    "        and faultpos_cart cannot both be given.  Default value is 0.5 if \n",
    "        faultpos_cart is also not given.\n",
    "    faultpos_cart : float, optional\n",
    "        The position to place the slip plane within the system given as a\n",
    "        Cartesian coordinate along the out-of-plane direction.  faultpos_rel\n",
    "        and faultpos_cart cannot both be given.\n",
    "    num_a1 : int, optional\n",
    "        The number of fractional coordinates to evaluate along a1vect_uvw.\n",
    "        Default value is 10.\n",
    "    num_a2 : int, optional\n",
    "        The number of fractional coordinates to evaluate along a2vect_uvw.\n",
    "        Default value is 10.\n",
    "    atomshift : array-like object, optional\n",
    "        A Cartesian vector shift to apply to all atoms.  Can be used to shift\n",
    "        atoms perpendicular to the fault plane to allow different termination\n",
    "        planes to be cut.  Cannot be given with shiftindex.\n",
    "    shiftindex : int, optional\n",
    "        Allows for selection of different termination planes based on the\n",
    "        preferred shift values determined by the underlying fault generation.\n",
    "        Cannot be given with atomshift. If neither atomshift nor shiftindex\n",
    "        given, then shiftindex will be set to 0.\n",
    "    etol : float, optional\n",
    "        The energy tolerance for the structure minimization. This value is\n",
    "        unitless. (Default is 0.0).\n",
    "    ftol : float, optional\n",
    "        The force tolerance for the structure minimization. This value is in\n",
    "        units of force. (Default is 0.0).\n",
    "    maxiter : int, optional\n",
    "        The maximum number of minimization iterations to use (default is \n",
    "        10000).\n",
    "    maxeval : int, optional\n",
    "        The maximum number of minimization evaluations to use (default is \n",
    "        100000).\n",
    "    dmax : float, optional\n",
    "        The maximum distance in length units that any atom is allowed to relax\n",
    "        in any direction during a single minimization iteration (default is\n",
    "        0.01 Angstroms).\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    dict\n",
    "        Dictionary of results consisting of keys:\n",
    "        \n",
    "        - **'A_fault'** (*float*) - The area of the fault surface.\n",
    "        - **'gamma'** (*atomman.defect.GammaSurface*) - A gamma surface\n",
    "          plotting object.\n",
    "    \"\"\"\n",
    "    # Construct stacking fault configuration generator\n",
    "    gsf_gen = am.defect.StackingFault(hkl, ucell, cutboxvector=cutboxvector,\n",
    "                                      a1vect_uvw=a1vect_uvw, a2vect_uvw=a2vect_uvw,\n",
    "                                      conventional_setting=conventional_setting)\n",
    "    \n",
    "    # Check shift parameters\n",
    "    if shiftindex is not None:\n",
    "        assert atomshift is None, 'shiftindex and atomshift cannot both be given'\n",
    "        atomshift = gsf_gen.shifts[shiftindex]\n",
    "    elif atomshift is None:\n",
    "        atomshift = gsf_gen.shifts[0]\n",
    "    \n",
    "    # Generate the free surface (zero-shift) configuration\n",
    "    gsf_gen.surface(shift=atomshift, minwidth=minwidth, sizemults=sizemults,\n",
    "                    even=even, faultpos_rel=faultpos_rel)\n",
    "    \n",
    "    abovefault = gsf_gen.abovefault\n",
    "    cutindex = gsf_gen.cutindex\n",
    "    A_fault = gsf_gen.surfacearea\n",
    "\n",
    "    # Identify lammps_date version\n",
    "    lammps_date = lmp.checkversion(lammps_command)['date']\n",
    "    \n",
    "    # Define lists\n",
    "    a1vals = []\n",
    "    a2vals = []\n",
    "    E_totals = []\n",
    "    disps = []\n",
    "\n",
    "    # Loop over all shift combinations\n",
    "    for a1, a2, sfsystem in gsf_gen.iterfaultmap(num_a1=num_a1, num_a2=num_a2):\n",
    "        a1vals.append(a1)\n",
    "        a2vals.append(a2)\n",
    "\n",
    "        # Evaluate the system at the shift\n",
    "        sim_directory = Path('a%.10f-b%.10f' % (a1, a2))\n",
    "        relax = stackingfaultrelax(lammps_command, sfsystem, potential,\n",
    "                                   mpi_command=mpi_command, \n",
    "                                   sim_directory=sim_directory,\n",
    "                                   cutboxvector=cutboxvector,\n",
    "                                   etol=etol, ftol=ftol, maxiter=maxiter,\n",
    "                                   maxeval=maxeval, dmax=dmax,\n",
    "                                   lammps_date=lammps_date)\n",
    "        \n",
    "        # Extract terms\n",
    "        E_totals.append(relax['E_total'])\n",
    "        pos = relax['system'].atoms.pos\n",
    "        disps.append(pos[abovefault, cutindex].mean()\n",
    "                   - pos[~abovefault, cutindex].mean())\n",
    "    \n",
    "    E_totals = np.array(E_totals)\n",
    "    disps = np.array(disps)\n",
    "    \n",
    "    # Get zeroshift values\n",
    "    E_total_0 = E_totals[0]\n",
    "    disp_0 = disps[0]\n",
    "    \n",
    "    # Compute the stacking fault energies\n",
    "    E_gsfs = (E_totals - E_total_0) / A_fault\n",
    "    \n",
    "    # Compute the change in displacement normal to fault plane\n",
    "    delta_disps = disps - disp_0\n",
    "    \n",
    "    results_dict = {}\n",
    "    results_dict['A_fault'] = A_fault\n",
    "    results_dict['gamma'] = am.defect.GammaSurface(a1vect = gsf_gen.a1vect_uvw,\n",
    "                                                   a2vect = gsf_gen.a2vect_uvw,\n",
    "                                                   box = gsf_gen.ucell.box,\n",
    "                                                   a1 = a1vals,\n",
    "                                                   a2 = a2vals,\n",
    "                                                   E_gsf = E_gsfs,\n",
    "                                                   delta = delta_disps)\n",
    "\n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Run calculation function(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict = stackingfaultmap(lammps_command, ucell, potential, hkl,\n",
    "                                mpi_command = mpi_command,\n",
    "                                sizemults = sizemults,\n",
    "                                minwidth = minwidth,\n",
    "                                a1vect_uvw = a1vect_uvw,\n",
    "                                a2vect_uvw = a2vect_uvw,\n",
    "                                cutboxvector = cutboxvector,\n",
    "                                shiftindex = shiftindex,\n",
    "                                num_a1 = num_a1,\n",
    "                                num_a2 = num_a2, \n",
    "                                etol = energytolerance,\n",
    "                                ftol = forcetolerance,\n",
    "                                maxiter = maxiterations,\n",
    "                                maxeval = maxevaluations,\n",
    "                                dmax = maxatommotion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['A_fault', 'gamma'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Report results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1. Define units for outputting values\n",
    "\n",
    "- __length_unit__ is the unit of area to display delta displacemets in.\n",
    "\n",
    "- __area_unit__ is the unit of area to display fault area in.\n",
    "\n",
    "- __energy_unit__ is the unit of energy to display cohesive energies in.\n",
    "\n",
    "- __energyperarea_unit__ is the energy per area to report the surface energy in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "length_unit = 'nm'\n",
    "area_unit = 'nm^2'\n",
    "energy_unit = 'eV'\n",
    "energyperarea_unit = 'mJ/m^2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2. Print $A_{fault}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A_fault = 5.365198866947918 nm^2\n"
     ]
    }
   ],
   "source": [
    "print('A_fault =', uc.get_in_units(results_dict['A_fault'], area_unit), area_unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3. Make plots from GammaSurface object gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  -0.5  0.5]\n",
      "[ 0.5 -0.5  0. ]\n"
     ]
    }
   ],
   "source": [
    "gamma = results_dict['gamma']\n",
    "print(gamma.a1vect)\n",
    "print(gamma.a2vect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save gamma as data model\n",
    "with open('gamma.json', 'w') as f:\n",
    "    gamma.model().json(fp=f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 846x415.692 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot with calculation a1vect, a2vect\n",
    "gamma.E_gsf_surface_plot(length_unit=length_unit,\n",
    "                         energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 846x415.692 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot with standard A1--fcc--111sf a1vect, a2vect\n",
    "gamma.E_gsf_surface_plot(a1vect=[0.5, 0.5, -1],\n",
    "                         a2vect=gamma.a2vect,\n",
    "                         length_unit=length_unit,\n",
    "                         energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standard 1D [11-2](111) fcc path\n",
    "gamma.E_gsf_line_plot(vect=[0.5, 0.5, -1], length_unit=length_unit,\n",
    "                      energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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